• Smart Structures and Systems
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• 제15권3호
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• pp.897-911
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• 2015

This study presents a novel approach based on advancements in Evolutionary Computation for data-driven modeling of complex multi-dimensional memory-dependent systems. The investigated example is a benchmark coupled three-dimensional system that incorporates 6 Bouc-Wen elements, and is subjected to external excitations at three points. The proposed technique of this research adapts Genetic Programming for discovering the optimum structure of the differential equation of an auxiliary variable associated with every specific degree-of-freedom of this system that integrates the imposed effect of vibrations at all other degrees-of-freedom. After the termination of the first phase of the optimization process, a system of differential equations is formed that represent the multi-dimensional hysteretic system. Then, the parameters of this system of differential equations are optimized in the second phase using Genetic Algorithms to yield accurate response estimates globally, because the separately obtained differential equations are coupled essentially, and their true performance can be assessed only when the entire system of coupled differential equations is solved. The resultant model after the second phase of optimization is a low-order low-complexity surrogate computational model that represents the investigated three-dimensional memory-dependent system. Hence, this research presents a promising data-driven modeling technique for obtaining optimized representative models for multi-dimensional hysteretic systems that yield reasonably accurate results, and can be generalized to many problems, in various fields, ranging from engineering to economics as well as biology.

• Smart Structures and Systems
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• 제33권4호
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• pp.301-311
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• 2024

This paper proposes a deep convolutional long short-term memory (ConvLSTM)-based crack growth prediction technique for predictive maintenance of structures. Since cracks are one of the critical damage types in a structure, their regular inspection has been mandatory for structural safety and serviceability. To effectively establish the structural maintenance plan using the inspection results, crack propagation or growth prediction is essential. However, conventional crack prediction techniques based on mathematical models are not typically suitable for tracking complex nonlinear crack propagation mechanism on civil structures under harsh environmental conditions. To address the technical issue, a field data-driven crack growth prediction technique using ConvLSTM is newly proposed in this study. The proposed technique consists of the four steps: (1) time-series crack image acquisition, (2) target image stabilization, (3) deep learning-based crack detection and quantification and (4) crack growth prediction. The performance of the proposed technique is experimentally validated using a concrete mock-up specimen by applying step-wise bending loads to generate crack growth. The validation test results reveal the prediction accuracy of 94% on average compared with the ground truth obtained by field measurement.

• 한국시뮬레이션학회논문지
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• 제18권4호
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• pp.185-195
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• 2009

최근 시스템들이 복잡해지면서 시뮬레이션을 통한 시스템의 분석이 주목을 받고 있다. 시뮬레이션 분석에서 가장 핵심적인 부분 중의 하나가 시뮬레이션 모델의 검증이며, 이 과정을 통하여 시뮬레이션 모델이 얼마나 실제 시스템을 대변할 수 있는지를 판단한다. 모델의 검증에서 시뮬레이션 모델과 실제시스템의 데이터를 비교할 때 발생하는 차이는 입력 데이터의 차이에 의한 영향도 있으며, 이를 통한 모델의 검증 결과는 높은 신뢰성을 보장하지 못한다. 따라서 이 논문에서는 실제와 동일한 입력 데이터를 바탕으로 하는 Trace-Driven 시뮬레이션을 기반으로 모델을 설계하였다. 한편, 출력데이터들을 하나의 통계량을 통한 검증이 아닌 클래스 별 검증을 하기 위해 데이터마이닝 분야에서 분류기의 성능을 판단하는 F 척도를 응용하여 시뮬레이션 모델의 검증을 수행하였다. 그 결과, 제안된 검증 방법은 정밀한 모델의 검증을 가능하게 하고, 검증 시에 피드백을 제공함으로써 용이한 수정 작업을 가능하게 한다.

• Wind and Structures
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• 제9권2호
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• pp.159-178
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• 2006

For the slender and flexible cable supported bridges, identification of all the flutter derivatives for the vertical, lateral and torsional motions is essential for its stability investigation. In all, eighteen flutter derivatives may have to be considered, the identification of which using a three degree-of-freedom elastic suspension system has been a challenging task. In this paper, a system identification technique, known as covariance-driven stochastic subspace identification (COV-SSI) technique, has been utilized to extract the flutter derivatives for a typical bridge deck. This method identifies the stochastic state-space model from the covariances of the output-only (stochastic) data. All the eighteen flutter derivatives have been simultaneously extracted from the output response data obtained from wind tunnel test on a 3-DOF elastically suspended bridge deck section-model. Simplicity in model suspension and measurements of only output responses are additional motivating factors for adopting COV-SSI technique. The identified discrete values of flutter derivatives have been approximated by rational functions.

• 한국통신학회논문지
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• 제35권6B호
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• pp.867-874
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• 2010

무선 센서 네트워크에서는 멀티 홉 전송동안 높은 패킷 손실률이 발생하기 때문에 신뢰성 있는 데이터 전송방안이 필요하다. 특히, 화재 경보 시스템과 같은 event-driven 데이터가 발생하는 경우, 신뢰성 있는 데이터 전송을 위해서는 손실된 패킷을 복원하기 위한 재전송 방안이 제공되어야 한다. 손실된 데이터의 재전송은 데이터를 캐쉬하고 있는 노드에 요청이 되기 때문에, 데이터를 캐쉬하고 있는 노드는 모든 데이터 패킷을 버퍼에서 유지하고 있어야 한다. 그러나 일반적으로 센서 네트워크의 노드들은 제한된 자원을 가지 있다. 따라서 신뢰성 있는 데이터 전송을 위해서는 손실 패킷의 재전송 방안과 노드의 버퍼 관리 기법이 함께 제공되어야 한다. 본 논문에서는 전송 데이터의 신뢰도에 따라 데이터의 캐쉬지점을 결정하여 손실된 데이터를 복원하는 손실 복원 기법을 사용하는 데이터 전송에서의 효율적인 버퍼 관리기법을 제안하고, 컴퓨터 시뮬레이션을 통하여 제안하는 방안의 우수성을 검증하였다.

• 대한건축학회논문집:구조계
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• 제36권3호
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• pp.169-176
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• 2020

The authors developed a nodal network model that simulates the flow of moist air and the thermal behavior of a target area. The nodal network model was enhanced using a parameter estimation technique based on the measured temperature, humidity, and schedule data. However, the nodal model is not good enough for predicting humidity of the target space, having 55.6% of CVRMSE. It is because re-evaporation effect could not be modeled due to uncertain factors in the field measurement. Hence, a data-driven model was introduced using an artificial neural network (ANN). It was found that the data-driven model is suitable for predicting the condensation compared to the nodal model satisfying ASHRAE Guideline with 3.36% of CVRMSE for temprature, relative humidity, and surface temperature on average. The model will be embedded in automated devices for real-time predictive control, to minimize the risk of surface condensation at dressroom in an apartment housing.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2005년도 추계 학술대회논문집
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• pp.311-317
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• 2005

It has been the most progressive interruption technique to use the ablation gas from the surface of PTFE nozzle driven by arc plasma during switching process in $SF_6$ gas circuit breakers. This advanced interruption technique can reduce the required mechanical energy to compress and blow the gas for extinguishing the arc plasma between the electrodes due to using the ablation effect instead. In order to consider the phenomena during calculation of switching process, it is required to confirm the principles of ablation from PTFE nozzle as well as of arc plasma during switching process. In this study, we have calculated the switching process considered the ablation of PTFE nozzle driven by arc plasma using multidisciplinary simulation technique and compared the results with the data without the ablation effect. More $50\%$ difference of pressure rise inside expansion chamber has been found from the results and it should be indispensable for this type of computational work to consider and include the ablation effect of PTFE nozzle. Further study on turbulence and radiation will be followed.

• 대한임베디드공학회논문지
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• 제6권6호
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• pp.345-359
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• 2011

In high performance embedded systems, the use of multiple on-chip memories is an essential architectural feature for exploiting inherent parallelism in multimedia applications. This feature allows multiple data accesses to be executed in parallel. However, it remains difficult to effectively exploit of multiple on-chip memories. The successful use of this architecture strongly depends on how to efficiently detect and exploit memory parallelism in target applications. In this paper, we propose a technique based on a linear array access descriptor [1], which is generated from profiled data, to detect and exploit memory parallelism. The proposed technique tackles an array reorganization problem to maximize memory parallelism in multimedia applications. We present preliminary experiments applying the proposed technique onto a representative coarse grained reconfigurable array processor (CGRA) with multimedia kernel codes. Our experimental results demonstrate that our technique optimizes data placement by putting independent data on separate storage. The results exhibit 9.8% higher performance on average compared to the existing method.

• 대한토목학회논문집
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• 제33권4호
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• pp.1413-1424
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• 2013

최근 우리나라에는 집중호우의 발생 빈도가 잦아지고 있다. 집중호우는 단시간에 발생하여 인명과 재산에 직접적인 피해를 주는 특징이 있다. 이러한 이유로 치수에 대한 관심은 점점 높아지고 있으며 정확한 유량 예측을 바탕으로 홍수에 대비할 수 있는 시스템 개발에 대한 연구가 활발하게 이루어지고 있다. 지금까지 홍수 예보에는 주로 물리적 모형이 사용되어 왔다. 물리적 모형은 매개변수 결정을 위해 많은 자료를 필요로 하고 또 매개변수의 결정 과정에서 많은 불확실성을 포함하고 있기 때문에 계산과정을 거치는 동안 다양한 오차가 반복하여 누적되는 단점이 있다. ANFIS는 인공신경회로망과 퍼지기법을 사용한 자료 지향형 모형으로 기존의 물리적 모형에서 사용한 방대한 양의 물리적 자료를 배제하고 유역의 강우자료와 유량자료만을 사용하여 모형을 구축하고 수위를 예측할 수 있다는 장점이 있다. 그러나 자료 지향형 모형은 입력 자료와 결과 사이의 논리적 상관성을 찾을 수 없다는 단점이 있다. 본 연구에서는 ANFIS 모형에 사용되는 함수의 옵션과 입력자료의 특성의 제한적인 변화에 따른 결과자료 분석을 통해 자료 지향형 모형의 특성을 분석하였다. 또한 일반적으로 많이 사용하는 물리적 모형 중 하나인 HEC-HMS의 유출량 산정 결과와의 비교를 통해 ANFIS의 적용성을 평가하였다. 본 연구는 남한강 상류에 위치한 청미천 유역의 2007년부터 2011년까지의 관측 강우자료와 유량자료를 사용하여 수행하였다.

• Smart Structures and Systems
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• 제10권4_5호
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• pp.331-351
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• 2012

In this study an output-only system identification technique for civil structures under ambient vibrations is carried out, mainly focused on using the Stochastic Subspace Identification (SSI) based algorithms. A newly developed signal processing technique, called Singular Spectrum Analysis (SSA), capable to smooth a noisy signal, is adopted for preprocessing the measurement data. An SSA-based SSI algorithm with the aim of finding accurate and true modal parameters is developed through stabilization diagram which is constructed by plotting the identified system poles with increasing the size of data matrix. First, comparative study between different approaches, with and without using SSA to pre-process the data, on determining the model order and selecting the true system poles is examined in this study through numerical simulation. Finally, application of the proposed system identification task to the real large scale structure: Canton Tower, a benchmark problem for structural health monitoring of high-rise slender structures, using SSA-based SSI algorithm is carried out to extract the dynamic characteristics of the tower from output-only measurements.